Skip to content

Latest commit

 

History

History
25 lines (21 loc) · 1022 Bytes

vllm_integration.md

File metadata and controls

25 lines (21 loc) · 1022 Bytes
Raw

vLLM Integration

You can use vLLM as an optimized worker implementation in FastChat. It offers advanced continuous batching and a much higher (~10x) throughput. See the supported models here.

Instructions

  1. Install vLLM.

    pip install vllm

  2. When you launch a model worker, replace the normal worker (fastchat.serve.model_worker) with the vLLM worker (fastchat.serve.vllm_worker). All other commands such as controller, gradio web server, and OpenAI API server are kept the same.

    python3 -m fastchat.serve.vllm_worker --model-path lmsys/vicuna-7b-v1.3

    If you see tokenizer errors, try

    python3 -m fastchat.serve.vllm_worker --model-path lmsys/vicuna-7b-v1.3 --tokenizer hf-internal-testing/llama-tokenizer

    If you use an AWQ quantized model, try ''' python3 -m fastchat.serve.vllm_worker --model-path TheBloke/vicuna-7B-v1.5-AWQ --quantization awq '''